to see a clear linear association between the values: this is clearly not the case. In fact, the bivariate correlation between the two logged p values is only .30. The right panel is the QQ plot for the unadjusted GWGEI model. There is a notable improvement in the departure of the values from the uniform line when compared with the comparable estimates in Fig. 2, but we still do not have any SNPs that cross the genome-wide significance level, so our failure to detect true GxE SNPs in our GWGEI model is not due to overadjustment of our models. Similarly, even when the analyses are conducted with the same individuals in the full sample, the parameter estimates show tremendous variation between the two models. We do find some associations that approach genome-wide significance, but these drop precipitously with important adjustments for stratification and rGE, and some show suppressor effects so that the unadjusted estimates have p values of less than .001 but the adjusted estimates are less than .00001. In other words, the effects of each SNP are very small and are quite sensitive to the inclusion or exclusion of control variables, which may have to do with our fairly
